During the hydraulic fracturing process, the fracturing fluid may cause water blockage, if the nearby secondary fractures subsequently close and get disconnected due to changes in effective stress distribution during flowback and production. The fluid inside the fractures could also get squeezed out upon fracture closure. The circumstances and detailed mechanisms associated with this phenomenon are still poorly understood. In this work, a coupling scheme for incorporating a pressure-dependent apparent permeability model in reservoir simulation is implemented. The numerical models are subsequently used to investigate the impacts of water blockage and apparent permeability modeling on gas production and water flowback.

A high-resolution 3D reservoir model is constructed based on the field data obtained from the Horn River shale gas reservoir. Stochastic 3D discrete fracture network (DFN) model is upscaled into equivalent continuum dual-porosity dual-permeability (DPDK) model by analytical techniques. A realistic DFN configuration is examined to simulate the potential scenarios of water blocking. An apparent permeability (Kapp) model that accounts for contributions of Knudsen diffusion, slip flow and surface pore roughness is introduced. In order to capture the pressure dependency, a novel coupling scheme is developed to facilitate the updating of Kapp and effective stress after a certain designated time interval. In addition, a novel method involving rock-type indicators is introduced to represent the open and closed states of secondary fractures, facilitating the modeling of stress-dependent closure of the secondary fracture system.

Fracture closure and the resulting water blockage would impact the gas production and water recovery, particularly if the near-well fractures are disconnected. Neglecting the effects of Kapp could essentially overestimate the contribution of hydraulic fracture for a certain observed gas production. The existence of secondary fractures could also enhance water loss, which is contrary to some conclusions in previous research where Kapp modeling and disconnected fractures are ignored. The impacts of shut-in duration and matrix multiphase flow functions are systematically studied. It is concluded that gas and water production would increase if less water is imbibed into the matrix during the shut-in period in the presence of disconnected secondary fractures. It is also observed that a shorter shut-in period may be beneficial to both water and gas recovery, where previous studies have reported no observable increase in gas production when secondary fracture closure was not considered.

This work presents a set of detailed simulation studies to examine the scenarios or conditions that may be responsible for water blockage, particularly in the presence of disconnected secondary fractures. A novel, yet practical, scheme is implemented to couple stress-dependent matrix apparent permeability and fluid flow, as well as to model pressure-dependent fracture closure. The modeling scheme can be readily integrated in most commercial reservoir simulation packages. The results have revealed several potential scenarios of water loss, along with the associated implications on optimal operational strategies and estimation of stimulated reservoir volume.

We analyzed flowback (FB) and post-flowback (PFB) production data from six multi-fractured horizontal wells completed in Eagle Ford Formation. The wells are supercharged at the beginning of the flowback process and the reservoir pressure remains above bubble point during the post-flowback period. Interestingly, we observe a pronounced unit slope (pseudo-steady state) in the rate-normalized pressure (RNP) plots of water for post-flowback period, while such unit slope is not observed for the flowback period. We developed a conceptual and mathematical model to describe these observations and to estimate the average fracture pore volume (Vf) during the post-flowback process. This model assumes no water influx from matrix into the fracture system, which is consistent with the lack of mobile water in the target reservoir. It also assumes stable influx of oil from matrix into the fracture system with insignificant mass accumulation of oil in the fracture system. Therefore, water production at pseudo-steady state conditions occurs under the driving forces of water expansion, oil expansion, and fracture closure. We also performed decline curve analysis on water production data to estimate initial Vf, as the fractures tend to be fully saturated with water at the beginning of the flowback process. The difference between ultimate water recovery and average Vf from the PFB model represents the loss in fracture volume due to fracture closure. The results show that about 65% of fracture closure occurs after 7 months of PFB production. Fracture closure is the dominant drive mechanism during FB and early PFB periods when reservoir pressure drops rapidly.

Organic carbon content and thermal maturity are critical to the evaluation of shale gas and shale oil plays, determining the productivity of the formation and type of hydrocarbons that are produced. In this analysis, we demonstrate that these parameters also affect acoustic properties; thus, well logs and potentially seismic data can be inverted to estimate organic richness and maturity. Our study is based on data from two cored wells in the Woodford Formation, Permian Basin, west Texas, that had been previously studied to develop sedimentological and geochemical models for black shale deposition. The KCC 503 well is relatively shallow, and the Woodford Shale here has a maturity of 0.71% Ro, just entering the oil window. The RTC 1 well is relatively deep, and the Woodford in this well has a maturity of 1.48% Ro, in the wet gas window. In our analysis, we link rock composition, including total organic carbon (TOC) content and thermal maturity to data on p-wave (VP) and s-wave (VS) velocities and density and derivative properties including Vp/Vs ratios, impedance, geomechanical parameters including Poisson's Ratio and Young's Modulus and Lamé parameters mu and lambda. We then test whether stratigraphic intervals within the Woodford can be geophysically distinguished and potentially mapped with seismic data.

TOC content is reflected in two well logs: gamma ray, which responds to uranium and is in turn linked to organic carbon content; and neutron porosity log, which responds to hydrogen content. Neutron porosity values are higher than core porosities because of the organic carbon content, much higher in the lower maturity KCC 503 well because of the development of a bitumen phase that is no longer present in the higher maturity RTC 1 well due to cracking to hydrocarbons. Gamma ray and neutron porosity are both inversely related to Vp and Vs. In the KCC 503 well, neutron porosity correlates more strongly to these parameters due to the presence of bitumen; in the RTC 1 well, both gamma ray and neutron porosity provide comparably strong correlations. Correlations to impedance are similar. Both gamma ray and neutron porosity predict Young's Modulus but not Poisson's Ratio. Vp, Vs, and impedance increase as a function of thermal maturity. Vp/Vs decreases as a function of thermal maturity, due to the greater increase in Vs compared to Vp, but is not sensitive to TOC in an individual well.

Stratigraphic intervals of the Woodford Shale have distinctive acoustic parameters. Vp/Vs ratios distinguish the three main subdivisions: the organic-rich Middle Woodford is characterized by low Vp/Vs ratios in comparison to the Upper (intermediate values) and Lower (high values). Poisson's Ratio values follow similar trends. This suggests that internal units within the Woodford Shale can be mapped with seismic data.

Since decades, steam-assisted oil recovery processes have been successfully deployed in heavy oil reservoirs to extract bitumen/heavy oil. Current resource allocation practices mostly involve reservoir model-based open loop optimization at the planning stage and its periodic recurrence. However, such decades-old strategies need a complete overhaul as they ignore dynamic changes in reservoir conditions and surface facilities, ultimately rendering heavy oil production economically unsustainable in the low-oil-price environment. Since steam supply costs account for more than 50% of total operating costs, a data-driven strategy that transforms the data available from various sensors into meaningful steam allocation decisions requires further attention.

In this research, we propose a purely data-driven algorithm that maximizes the economic objective function by allocating an optimal amount of steam to different well pads. The method primarily constitutes two components: forecasting and nonlinear optimization. A dynamic model is used to relate different variables in historical field data that were measured at regular time intervals and can be used to compute economic performance indicators (EPI). The variables in the model are cumulative in nature since they can represent the temporal changes in reservoir conditions. Accurate prediction of EPI is ensured by retraining regression model using the latest available data. Then, predicted EPI is optimized using a nonlinear optimization algorithm subject to amplitude and rate saturation constraints on decision variables i.e., amount of steam allocated to each well pad.

Proposed steam allocation strategy is tested on 2 well pads (each containing 10 wells) of an oil sands reservoir located near Fort McMurray in Alberta, Canada. After exploratory analysis of production history, an output error (OE) model is built between logarithmically transformed cumulative steam injection and cumulative oil production for each well pad. Commonly used net-present-value (NPV) is considered as EPI to be maximized. Optimization of the objective function is subject to distinct operating conditions and realistic constraints. By comparing results with field production history, it can be observed that optimum steam injection profiles for both well pads are significantly different than that of a field. In fact, the proposed algorithm provides smooth and consistent steam injection rates, unlike field injection history. Also, the lower steam-oil ratio is achieved for both well pads, ultimately translating into ~19 % higher NPV when compared with field data.

Inspired from state-of-the-art control techniques, proposed steam allocation algorithm provides a generic data-driven framework that can consider any number of well pads, EPIs, and amount of past data. It is computationally inexpensive as no numerical simulations are required. Overall, it can potentially reduce the energy required to extract heavy oil and increase the revenue while inflicting no additional capital cost and reducing greenhouse gas emissions.

Summary

Steam-assisted gravity drainage (SAGD) is a thermal-recovery process to produce bitumen from oil sands. In this technology, steam injected in the reservoir creates a constantly evolving steam chamber while heated bitumen drains to a production well. Understanding the geometry and the rate of growth of the steam chamber is necessary to manage an economically successful SAGD project. This work introduces an approximate physics-discrete simulator (APDS) to model the steam-chamber evolution. The algorithm is formulated and implemented using graph theory, simplified porous-media flow equations, heat-transfer concepts, and ideas from discrete simulation. The APDS predicts the steam-chamber evolution in heterogeneous reservoirs and is computationally efficient enough to be applied over multiple geostatistical realizations to support decisions in the presence of geological uncertainty. The APDS is expected to be useful for selecting well-pair locations and operational strategies, 4D-seismic integration in SAGD-reservoir characterization, and caprock-integrity assessment.

Summary

Field studies have shown that, if an inclined fracture has a significant inclination angle from the vertical direction or the fracture has a poor growth along the inclined direction, this fracture probably cannot fully penetrate the formation, resulting in a partially penetrating inclined fracture (PPIF) in these formations. It is necessary for the petroleum industry to conduct a pressure-transient analysis on such fractures to properly understand the major mechanisms governing the oil production from them. In this work, we develop a semianalytical model to characterize the pressure-transient behavior of a finite-conductivity PPIF. We discretize the fracture into small panels, and each of these panels is treated as a plane source. The fluid flow in the fracture system is numerically characterized with a finite-difference method, whereas the fluid flow in the matrix system is analytically characterized on the basis of the Green’s-function method. As such, a semianalytical model for characterizing the transient-flow behavior of a PPIF can be readily constructed by coupling the transient flow in the fracture and that in the matrix. With the aid of the proposed model, we conduct a detailed study on the transient-flow behavior of the PPIFs. Our calculation results show that a PPIF with a finite conductivity in a bounded reservoir can exhibit the following flow regimes: wellbore afterflow, fracture radial flow, bilinear flow, inclined-formation linear flow, vertical elliptical flow, vertical pseudoradial flow, inclined pseudoradial flow, horizontal-formation linear flow, horizontal elliptical flow, horizontal pseudoradial flow, and boundary-dominated flow. A negative-slope period can appear on the pressure-derivative curve, which is attributed to a converging flow near the wellbore. Even with a small dimensionless fracture conductivity, a PPIF can exhibit a horizontal-formation linear flow. In addition to PPIFs, the proposed model also can be used to simulate the pressure-transient behavior of fully penetrating vertical fractures (FPVFs), partially penetrating vertical fractures (PPVFs), fully penetrating inclined fractures (FPIFs), and horizontal fractures (HFs).

Shale heterogeneities often impede the development of steam chamber in many steam-assisted gravity drainage (SAGD) projects. Unfortunately, static data alone is generally insufficient for inferring the corresponding distribution of shale barriers. This study presents a novel data-driven modeling workflow, which integrates deep learning (DL) and data analytics techniques to analyze production profiles from horizontal well pairs and temperature profiles from vertical observation wells, for the inference of shale barrier characteristics.

Field data gathered from several Athabasca oil sands projects are extracted to build a set of synthetic SAGD models, where the geometries, proportions and spatial distribution of shale barriers are modeled stochastically. Numerical flow simulation is performed on each realization; the corresponding production/injection time-series data, as well as temperature profiles from one vertical observation well, are recorded. A large dataset is assembled for the development of data-driven models: wavelet analysis and other data analysis techniques are performed to extract relevant input features from the temperature and production profiles; a novel parameterization scheme is also proposed to formulate the output variables that would effectively describe the detailed distribution of shale barriers. DL, such as convolutional neural network, together with other data analytics techniques are applied to capture the complex and nonlinear relationships between these input and output variables.

The feasibility of the developed workflow is validated using synthetic test cases. Salient features capturing the impacts of shale barriers are extracted. It is observed from the production time-series data that, as the steam chamber approaches a shale barrier, a decline pattern is noticeable until the steam chamber advances around the shale barrier. An obstruction in the steam chamber development can also be noted in the temperature profiles, as steam is trapped by shale barriers that are located reasonably close to the horizontal well pair. This observation is confirmed by comparing the petrophysical logs and the temperature profiles at the observation wells. Analyzing both temperature and production data could help to infer the size of shale barriers in the inter-well regions. Finally, the model outputs are used to generate an ensemble of heterogeneous SAGD realizations that correspond to the input production and temperature time-series data.

This study offers a complementary and computationally-efficient tool for inference of stochastically-distributed shale barriers in SAGD models, which can be subjected to detailed history-matching workflows. It is the first time that data-driven models are used to analyze both production data from horizontal production well pairs and temperature profiles from a vertical observation well for inferring SAGD reservoir heterogeneities. The results illustrate the potential for application of data analytics in reservoir modeling and flow simulation analysis. The developed workflow also can be extended to characterize reservoir heterogeneities in other recovery processes.

Tight sands are abundant in nanopores leading to a high capillary pressure and normally a low fluid injectivity. As such, spontaneous imbibition might be an effective mechanism for improving oil recovery from tight sands after fracturing. The chemical agents added to the injected water can alter the interfacial properties, which could help further enhance the oil recovery by spontaneous imbibition. This study explores the possibility of using novel chemicals to enhance oil recovery from tight sands via spontaneous imbibition. We experimentally examine the effects of more than ten different chemical agents on spontaneous imbibition, including a cationic surfactant (C12TAB), two anionic surfactants (O242 and O342), an ionic liquid (BMMIM BF4), a high pH solution (NaBO2), and a series of house-made deep eutectic solvents (DES3-7, 9, 11 and 14). Experimental results indicate that the ionic liquid and cationic surfactant used in this study are detrimental to spontaneous imbibition and decrease the oil recovery from tight sands. The high pH NaBO2 solution does not demonstrate significant effect on improving oil recovery, even though it significantly reduces oil-water interfacial tension (IFT). The anionic surfactants (O242 and O342) are effective in enhancing oil recovery from tight sands through oil-water IFT reduction and emulsification effects. The DESs drive the rock surface to be more water-wet and a specific formulation (DES9) leads to much improvement on oil recovery under counter-current imbibition condition. This preliminary study would provide some knowledge about how to optimize the selection of chemicals for improving oil recovery from tight reservoirs.

In comparison to Steam-Assisted Gravity-Drainage (SAGD), the technique of injecting of warm solvent vapor into the formation for heavy oil production offers many advantages, including lower capital and operational costs, reduced water usage, and less greenhouse gas emission. However, to select the optimal operational parameters for this process in heterogeneous reservoirs is non-trivial, as it involves the optimization of multiple distinct objectives including oil production, solvent recovery (efficiency), and solvent-oil ratio. Traditional optimization approaches that aggregate numerous competing objectives into a single weighted objective would often fail to identify the optimal solutions when several objectives are conflicting. This work aims to develop a hybrid optimization framework involving Pareto-based multiple objective optimization (MOO) techniques for the design of warm solvent injection (WSI) operations in heterogeneous reservoirs.

First, a set of synthetic WSI models are constructed based on field data gathered from several typical Athabasca oil sands reservoirs. Dynamic gridding technique is employed to balance the modeling accuracy and simulation time. Effects of reservoir heterogeneities introduced by shale barriers on solvent efficiency are systematically investigated. Next, a state-of-the-art MOO technique, non-dominated sorting genetic algorithm II, is employed to optimize several operational parameters, such as bottomhole pressures, based on multiple design objectives. In order to reduce the computational cost associated with a large number of numerical flow simulations and to improve the overall convergence speed, several proxy models (e.g., response surface methodology and artificial neural network) are integrated into the optimization workflow to evaluate the objective functions.

The study demonstrates the potential impacts of reservoir heterogeneities on the WSI process. Models with different heterogeneity settings are examined. The results reveal that the impacts of shale barriers may be more/less evident under different circumstances. The proxy models can be successfully constructed using a small number of simulations. The implementation of proxy models significantly reduces the modeling time and storages required during the optimization process. The developed workflow is capable of identifying a set of Pareto-optimal operational parameters over a wide range of reservoir and production conditions.

This study offers a computationally-efficient workflow for determining a set of optimum operational parameters relevant to warm solvent injection process. It takes into account the tradeoffs and interactions between multiple competing objectives. Compared with other conventional optimization strategies, the proposed workflow requires fewer costly simulations and facilitates the optimization of multiple objectives simultaneously. The proposed hybrid framework can be extended to optimize operating conditions for other recovery processes.

Cold heavy oil production with sand (CHOPS) is a non-thermal primary process that is widely adopted in many weakly consolidated heavy oil deposits around the world. However, only 5 to 15% of the initial oil in place is typically recovered. Several solvent-assisted schemes are proposed as follow-up strategies to increase the recovery factor in post-CHOPS operations. The development of complex, heterogeneous, high-permeability channels or wormholes during CHOPS renders the analysis and scalability of these processes challenging. One of the key issues is how to properly estimate the dynamic growth of wormholes during CHOPS. Existing growth models generally offer a simplified representation of the wormhole network, which, in many cases, is denoted as an extended wellbore. Despite it is commonly acknowledged that wormhole growth due to sand failure is likely to follow fractal statistics, there are no established workflows to incorporate geomechanical constraints into the construction of these fractal wormhole patterns.

A novel dynamic wormhole growth model is developed to generate a set of realistic fractal wormhole networks during the CHOPS operations. It offers an improvement to the Diffusion Limited Aggregation (DLA) algorithm with a sand-arch-stability criterion. The outcome is a fractal pattern that mimics a realistic wormhole growth path, with sand failure and fluidization being controlled by geomechanical constraints. The fractal pattern is updated dynamically by coupling compositional flow simulation on a locally-refined grid and a stability criterion for the sand arch: the wormhole would continue expanding following the fractal pattern, provided that the pressure gradient at the tip exceeds the limit corresponding to a sand-arch-stability criterion. Important transport mechanisms including foamy oil (non-equilibrium dissolution of gas) and sand failure are integrated.

Public field data for several CHOPS fields in Canada is used to examine the results of the dynamic wormhole growth model and flow simulations. For example, sand production history is used to estimate a practical range for the critical pressure gradient representative of the sand-arch-stability criterion. The oil and sand production histories show good agreement with the modeling results.

In many CHOPS or post-CHOPS modeling studies, constant wormhole intensity is commonly assigned uniformly throughout the entire domain; as a result, the ensuing models are unlikely to capture the complex heterogeneous distribution of wormholes encountered in realistic reservoir settings. This work, however, proposes a novel model to integrate a set of statistical fractal patterns with realistic geomechanical constraints. The entire workflow has been readily integrated with commercial reservoir simulators, enabling it to be incorporated in practical field-scale operations design.